Deep learning for bitmap tracing: extract simple svg paths from bitmaps
If we seek proven working source code to plug into a GPLv2-licence compatible solution, we should at least consider autotrace. Its source code is open for review. It can be tested against the example images we have and, if it works fine, called by our GPLv2 software. We can even use the calling code in Inkscape's plug-in image tracing implementation as a good starting point for design and implementation of our calling program, whether it be C, C++, Java, Python, or ECMA (JS).

The trace algorithm in Adobe Illustrator is comparable but is not open source.

If we seek theory, there are several academic publications discussing some of the theory, the last being most aligned with machine learning ideology. I would not dismiss earlier work simply because it doesn't connect with the current machine learning idioms. Investigating what is fully implemented and successfully used by many follows a wise old business proverb: The bird in the hand is worth two in the bush.


Potrace: a polygon-based tracing algorithm, Peter Selinger, 2003
Vector Representation of Binary Images Containing Halftone Dots, Kei Kawamura, Hiroshi Watanabe, Hideyoshi Tominaga, 2004
Testing AutoTrace:
A Machine-learning Approach to Automated
Tongue Contour Data Extraction, Jae-Hyun Sung, Jeff Berry, Marissa Cooper, Gustave Hahn-Powell, and Diana Archangeli, 2013


Many of the online drawing programs collect data.  It would not be surprising if, behind the gracious give-away of online bandwidth, they are establishing a continuously improving data set for training a new breed of autotracers.  None have published AI designs admitting as much, but they would not be legally obligated to do so because a single input example is indeterminable from the autotrace service that could resulting from the training.